Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23624
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: INODE : building an end-to-end data exploration system in practice
Authors: Amer-Yahia, Sihem
Koutrika, Georgia
Braschler, Martin
Calvanese, Diego
Lanti, Davide
Lücke-Tieke, Hendrik
Mosca, Alessandro
Mendes de Farias, Tarcisio
Papadopoulos, Dimitris
Patil, Yogendra
Rull, Guillem
Smith, Ellery
Skoutas, Dimitrios
Subramanian, Srividya
Stockinger, Kurt
et. al: No
DOI: 10.21256/zhaw-23624
Published in: SIGMOD Record
Issue Date: Dec-2021
Publisher / Ed. Institution: Association for Computing Machinery
ISSN: 0163-5808
Language: English
Subjects: Databases; Data exploration; Machine learning
Subject (DDC): 005: Computer programming, programs and data
006: Special computer methods
Abstract: A full-fledged data exploration system must combine different access modalities with a powerful concept of guiding the user in the exploration process, by being reactive and anticipative both for data discovery and for data linking. Such systems are a real opportunity for our community to cater to users with different domain and data science expertise. We introduce INODE - an end-to-end data exploration system - that leverages, on the one hand, Machine Learning and, on the other hand, semantics for the purpose of Data Management (DM). Our vision is to develop a classic unified, comprehensive platform that provides extensive access to open datasets, and we demonstrate it in three significant use cases in the fields of Cancer Biomarker Research, Research and Innovation Policy Making, and Astrophysics. INODE offers sustainable services in (a) data modeling and linking, (b) integrated query processing using natural language, (c) guidance, and (d) data exploration through visualization, thus facilitating the user in discovering new insights. We demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public. Finally, we briefly illustrate how this work paves the way for new research opportunities in DM.
URI: https://digitalcollection.zhaw.ch/handle/11475/23624
Fulltext version: Published version
License (according to publishing contract): Not specified
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Published as part of the ZHAW project: INODE – Intelligent Open Data Exploration (EU Horizon 2020)
Appears in collections:Publikationen School of Engineering

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